Chapter 13. Data Processing Pipelines

Data processing is a complex field that’s constantly evolving to meet the demands of larger data sets, intensive data transformations, and a desire for fast, reliable, and inexpensive results. The current landscape features data sets that are generated and collected from a variety of sources—from mobile usage statistics to integrated sensor networks to web application logs, and more. Data processing pipelines can turn these often unbounded, unordered, global-scale data sets into structured, indexed storage that can help inform crucial business decisions or unlock new product features. In addition to providing insight into system and user behavior, data processing is often business-critical. Delayed or incorrect data in your pipeline can manifest in user-facing issues that are expensive, labor-intensive, and time-consuming to fix.

This chapter starts by using product examples to examine some common types of applications of big data processing pipelines. We then explore how to identify pipeline requirements and design patterns, and enumerate some best practices of managing data processing pipelines throughout the development lifecycle. We cover tradeoffs you can make to optimize your pipeline and techniques for measuring the important signals of pipeline health. For a service to remain healthy and reliable once it’s deployed, SREs (as well as developers) should ...

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